From all 29 test sequences, our benchmark computes the commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2].
The tables below show all of these metrics.
| Benchmark |
MOTA |
MOTP |
MODA |
MODP |
| CAR |
90.95 % |
85.77 % |
91.44 % |
88.42 % |
| PEDESTRIAN |
64.46 % |
74.74 % |
65.40 % |
92.06 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
93.60 % |
98.83 % |
96.14 % |
36695 |
434 |
2511 |
3.90 % |
45888 |
1398 |
| PEDESTRIAN |
73.18 % |
90.79 % |
81.04 % |
17116 |
1736 |
6274 |
15.61 % |
22548 |
626 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
84.77 % |
12.46 % |
2.77 % |
169 |
394 |
| PEDESTRIAN |
45.70 % |
38.83 % |
15.46 % |
218 |
900 |
This table as LaTeX
|
[1] K. Bernardin, R. Stiefelhagen:
Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia:
Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.
